Testing AI systems and LLM integrations
Test the model, not just the API.
Prompt injection, data poisoning, model manipulation, output exploitation: different problems requiring different expertise. Most AI security assessments stop at the API. We go deeper, throwing adversarial inputs at the model, testing the training pipeline, and checking whether safety guardrails hold up when someone is trying to break them.
Aligned with the OWASP LLM Top 10. Model security audit. RAG pipeline review.
Six things we test.
The whole AI stack, from the input to the supply chain.
- 01
Prompt injection testing
We throw everything at your LLM's input handling to find prompt injection paths that bypass guardrails, leak system prompts, or make the model do things it should not.
- 02
Training data integrity
We check your training pipeline for data poisoning risks: where the data comes from, how it is validated, and how the model behaves when someone feeds it adversarial inputs.
- 03
Model output validation
We test for hallucinations, PII leaks in generated text, unsafe code suggestions, and output that could be used for social engineering or fraud.
- 04
Agentic & multi-agent security
We start at the single agent: prompt injection, memory poisoning, tool manipulation. Then we scale up, impersonating agents, altering messages, and injecting tasks into the shared queues multi-agent systems run on.
- 05
RAG & plugin security
RAG pipelines, external tool integrations, and plugins all expand what your model can do, and what an attacker can exploit: ingestion poisoning, retrieval hijacking, embedding attacks, and MCP tool surfaces like description poisoning, tool shadowing, and parameter coercion.
- 06
AI supply chain
Third-party models, pre-trained weights, LoRA adapters, external APIs, and the CI/CD pipelines that ship them. We check your entire AI supply chain for vulnerabilities you inherited from someone else.
Four attack categories. One assessment.
What we test for.
- 01
Prompt injection
An attacker does not need your credentials, just a well-worded input. A single crafted prompt, or a chain of them across a conversation, can be enough to push a model past its own guardrails.
- 02
Data poisoning
Tampered training data does not announce itself. The model looks fine until a specific trigger flips its behaviour, and by then it has already shipped to production.
- 03
Output exploitation
A model does not have to be broken to be dangerous. A confident hallucination, a leaked record, or a plausible phishing draft can all come from a system working exactly as designed.
- 04
Supply chain
You are trusting more than your own code: the base model, its weights, and every plugin or API it calls. One compromised link anywhere in that chain becomes your problem too.
Purpose-built for AI attack surfaces.
Model & pipeline review
We map your entire AI setup: what model you run, where the training data comes from, how it is deployed, what interfaces face users, and what third-party services are plugged in.
Adversarial testing
Prompt injection, jailbreaks, data extraction, output manipulation. We see whether the safety guardrails and input validation actually work.
Data & supply chain
We check the training data, RAG pipeline security, plugin authentication, and every third-party dependency in your AI stack.
Report & harden
Every finding comes with reproduction steps, risk rating, and a specific fix, whether that is input sanitisation, output filtering, or model-level guardrails.
What you receive.
- Findings with reproduction steps
- Every finding comes with the steps to reproduce it.
- Risk rating
- Each finding rated, so you know where to start.
- A specific fix
- Input sanitisation, output filtering, or model-level guardrails, whichever applies.
What changes.
Test beyond the API
The model, the training pipeline, and the integrations around it.
Know if the guardrails hold
When someone is actually trying to break them.
Fix with specifics
Every finding ends in a concrete change to make.
Your AI has gaps you have not considered.
Prompt injection, data poisoning, model manipulation. Standard security assessments do not cover any of it. Let us find the gaps in your AI stack before someone with different motivations does.
Talk to an operator